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Abhishek Cauligi

cauligi _at_ jhu _dot_ edu

Abhishek Cauligi

Alumni · now at Johns Hopkins University (via NASA Jet Propulsion Laboratory)

Abhishek is a PhD. candidate in Aeronautics and Astronautics. He received a BS in Aerospace Engineering from the University of Michigan in 2016 and an M.S. in Aeronautics & Astronautics from Stanford in 2018. Prior to Stanford, Abhishek interned with the GNC group at SpaceX and the ADCS group at Planetary Resources.

Abhishek's current research interests entail combining tools from trajectory optimization, optimal control, and machine learning towards problems in spacecraft robotics and systems with contact. In addition, he has worked on running experiments on the International Space Station with the Astrobee robot for grasping and control using gecko-inspired adhesives.

Awards:

  • 2016 NASA Space Technology Research Fellowship

Publications

7 publications · full lab bibliography

2022
  1. A. Cauligi, P. Culbertson, E. Schmerling, M. Schwager, B. Stellato, and M. Pavone, “CoCo: Online Mixed-Integer Control via Supervised Learning,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1447--1454, 2022.
  2. T. G. Chen, A. Cauligi, S. A. Suresh, M. Pavone, and M. R. Cutkosky, “Testing Gecko-Inspired Adhesives with Astrobee Aboard the ISS,” IEEE Robotics and Automation Magazine, vol. 29, no. 3, pp. 24--33, 2022.
2021
  1. A. Cauligi, “Data-Driven Approaches for Mixed Integer Convex Programming in Robot Control,” Ph.D. dissertation, Stanford University, Dept. of Aeronautics and Astronautics, 2021.
2020
  1. A. Cauligi, T. Chen, S. A. Suresh, M. Dille, R. G. Ruiz, A. M. Vargas, M. Pavone, and M. R. Cutkosky, “Design and Development of a Gecko-Adhesive Gripper for the Astrobee Free-Flying Robot,” Int. Symp. on Artificial Intelligence, Robotics and Automation in Space, 2020.
  2. A. Cauligi, P. Culbertson, B. Stellato, D. Bertsimas, M. Schwager, and M. Pavone, “Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control,” Proc. IEEE Conf. on Decision and Control, 2020.
2019
  1. R. Bonalli, A. Bylard, A. Cauligi, T. Lew, and M. Pavone, “Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach,” Robotics: Science and Systems, 2019.
  2. R. Bonalli, A. Cauligi, A. Bylard, and M. Pavone, “GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming,” Proc. IEEE Conf. on Robotics and Automation, 2019.